Datasets. The experiments are conducted on InterHand2.6M V1.0 [1]} dataset and Tzionas dataset [2]}.
InterHand2.6M V1.0 dataset [1]} is a publicly available large-scale realistic pose estimation dataset for two-hand interactions. The dataset provides RGB images with semi-automatically annotated 3D poses, and MANO [4]} parameters obtained from NeuralAnnot [5]}. In the experiments, we follow the common practice [1]} to use the downsized \(512\times 334\)  image resolution at 5 frames-per-second (FPS) version of the released dataset. Following the official configurations, the dataset is split into three branches, namely `H' for the human annotation branch, `M' for the machine annotation branch, and `ALL' for all data. The `M' branch data contains many unseen poses and more diverse sequences, which makes it more similar to real-world scenarios. Moreover, we notice that the `H' branch data contains missing or incomplete mesh annotations. In the experiments, we majorly conduct experiments on the `M' branch, but also report the results on `ALL' branch for comparisons. To focus on the interacting hands, the original dataset [1]} divides the whole dataset (IH26M-ALL) into single-hands subset (IH26M-SH) and interacting-hands subset (IH26M-IH). Following [8]}, we further select samples from the original “IH26M-IH” test set, and generate a more challenging subset called “IH26M-Inter”. “IH26M-Inter” contains samples with more than 30 valid `ground-truth' 3D hand keypoints. Since InterHand2.6M [1]} is captured in a lab environment, its background diversity is relatively limited. To evaluate the generalization ability, we perform qualitative and quantitative experiments on the
Tzionas dataset [2]}. Since Tzionas dataset does not provide a separate training set, we directly use it as the testing set to evaluate the model trained on the InterHand2.6M dataset [1]}.
